





import colorsys

from PIL import Image  # type: ignore


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						float:
    SCREAMING_SNAKE_CASE__											=			x
    SCREAMING_SNAKE_CASE__											=			y
    for step in range(lowerCAmelCase_	):  # noqa: B007
        SCREAMING_SNAKE_CASE__											=			a * a - b * b + x
        SCREAMING_SNAKE_CASE__											=			2 * a * b + y
        SCREAMING_SNAKE_CASE__											=			a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def 							__snake_case							(    lowerCAmelCase_	)				->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def 							__snake_case							(    lowerCAmelCase_	)				->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5	) for i in colorsys.hsv_to_rgb(lowerCAmelCase_ ,							1 ,							1	)	)


def 							__snake_case							(    lowerCAmelCase_ = 8_0_0 ,							lowerCAmelCase_ = 6_0_0 ,							lowerCAmelCase_ = -0.6 ,							lowerCAmelCase_ = 0 ,							lowerCAmelCase_ = 3.2 ,							lowerCAmelCase_ = 5_0 ,							lowerCAmelCase_ = True ,							)				->						Image.Image:
    SCREAMING_SNAKE_CASE__											=			Image.new('''RGB''' ,							(image_width, image_height)	)
    SCREAMING_SNAKE_CASE__											=			img.load()

    # loop through the image-coordinates
    for image_x in range(lowerCAmelCase_	):
        for image_y in range(lowerCAmelCase_	):
            # determine the figure-coordinates based on the image-coordinates
            SCREAMING_SNAKE_CASE__											=			figure_width / image_width * image_height
            SCREAMING_SNAKE_CASE__											=			figure_center_x + (image_x / image_width - 0.5) * figure_width
            SCREAMING_SNAKE_CASE__											=			figure_center_y + (image_y / image_height - 0.5) * figure_height

            SCREAMING_SNAKE_CASE__											=			get_distance(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                SCREAMING_SNAKE_CASE__											=			get_color_coded_rgb(lowerCAmelCase_	)
            else:
                SCREAMING_SNAKE_CASE__											=			get_black_and_white_rgb(lowerCAmelCase_	)

    return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  _A					:				Optional[int]				=			get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
